# Machine learning study of highly spin-polarized Heusler alloys at finite temperature

https://mdr.nims.go.jp/datasets/f6578f96-f9e1-4f8f-bed4-0597a386061e

## File

- [2022Ivan_PhysRevMaterials.6.L091402.pdf](https://mdr.nims.go.jp/filesets/a04629ed-2b98-4d8f-8df7-79172f43cfdc/download) ([Detail](https://mdr.nims.go.jp/filesets/a04629ed-2b98-4d8f-8df7-79172f43cfdc.md))

## Id

f6578f96-f9e1-4f8f-bed4-0597a386061e

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-04-28T05:50:37.703954Z

## Updated at

2024-01-05T13:14:00.813542Z

## Published at

2023-07-10T04:30:32.751920Z

## Doi



## First published url

https://doi.org/10.1103/physrevmaterials.6.l091402

## Date published

2022-09-14

## Recorded date published

2022-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Machine learning study of highly spin-polarized Heusler alloys at finite
    temperature
  title_type: original
  lang: en

## Description

- description: A huge magnetoresistance (MR) ratio exceeding 2000% at cryogenic temperature
    that was reported for half-metallic Heusler alloy based magnetic tunnel junctions
    showed large degradation at room temperature, which impedes practical application
    of Heusler alloy based MR devices. This motivates us to explore alternative Heusler
    alloys that show high spin polarization at finite temperatures. Here, we propose
    half-metallic Heusler alloys based on finite-temperature first-principles calculation
    via the disordered local moment method together with machine learning. We found
    several prospective materials at room temperature such as Co2MnGa0.2As0.8 and
    Co2FeAl0.4Sn0.6. We also investigated two combinatorial series, Co2MnGayAs1-y
    and Co2FeAlySn1-y, to understand the effect of alloy mixing on temperature dependence
    and found that Fermi level tuning significantly improved the spin polarization
    and its temperature dependence, especially in Co2FeAlySn1-y.
  description_type: abstract
  lang: en

## Creator

- name: Ivan Kurniawan
  role: author
  orcid: https://orcid.org/0000-0001-5419-0047
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Yoshio Miura
  role: author
  orcid: https://orcid.org/0000-0002-5605-5452
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kazuhiro Hono
  role: author
  orcid: https://orcid.org/0000-0001-7367-0193
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: American Physical Society (APS)

## Managing organization



## Keyword

- subject: spintronics, half-metal, Heusler alloy, machine learning, disordered local
    moment, Bayesian optimization
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Physical Review Materials
  issn: '24759953'
  article_number: L091402

## Conference



## Related item



## Funding

- identifier: 17H06152, 20H02190, 22H04966, JPMJCR21O1
  funder_name: JSPS and JST-CREST

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: a04629ed-2b98-4d8f-8df7-79172f43cfdc
  filename: 2022Ivan_PhysRevMaterials.6.L091402.pdf
  content_type: application/pdf
  size: 2855665
  md5: d3b9d926daf2053b9105f4127f290406

## Thumbnail

fileset_id: a04629ed-2b98-4d8f-8df7-79172f43cfdc
filename: 2022Ivan_PhysRevMaterials.6.L091402.pdf